Tolerance of Informality and Occupational Choices in a Large Informal Sector Economy
Bibliographic record
Abstract
Abstract We study an equilibrium two-sector occupational choice model – agents can be (formal or informal) entrepreneurs or workers. An informal entrepreneur faces taxation determined by the combination of her capital choice and society’s tolerance of informality. Our model is consistent with many empirical findings regarding the informal sector in Brazil, a developing economy with a large informal sector. With a calibrated version of our model, we show that as society’s tolerance of informality decreases, the informal sector employs less capital and labor, and informality decreases. We conduct several counterfactual exercises. Informality is substantially lower in economies that are less tolerant of informal activities, formal entrepreneurs have more access to financial markets, and taxation of output and labor is lower. We extend the model to consider stochastic taxation of informal activities – a higher (average) informal output taxation and its variability reduce informality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".